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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Deep neural networks are gaining importance and popularity in applications and *** to the enormous number of learnable parameters and datasets,the training of neural networks is computationally *** and distributed com...
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Deep neural networks are gaining importance and popularity in applications and *** to the enormous number of learnable parameters and datasets,the training of neural networks is computationally *** and distributed computation-based strategies are used to accelerate this training *** Adversarial Networks(GAN)are a recent technological achievement in deep *** generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous ***,a GAN is trained on a single *** deep learning accelerator designs are challenged by the unique properties of GAN,like the enormous computation stages with non-traditional convolution *** work addresses the issue of distributing GANs so that they can train on datasets distributed over many TPUs(Tensor Processing Unit).Distributed learning training accelerates the learning process and decreases computation *** this paper,the Generative Adversarial Network is accelerated using the distributed multi-core TPU in distributed data-parallel synchronous *** adequate acceleration of the GAN network,the data parallel SGD(Stochastic Gradient Descent)model is implemented in multi-core TPU using distributed TensorFlow with mixed precision,bfloat16,and XLA(Accelerated Linear Algebra).The study was conducted on the MNIST dataset for varying batch sizes from 64 to 512 for 30 epochs in distributed SGD in TPU v3 with 128×128 systolic *** extensive batch technique is implemented in bfloat16 to decrease the storage cost and speed up floating-point *** accelerated learning curve for the generator and discriminator network is *** training time was reduced by 79%by varying the batch size from 64 to 512 in multi-core TPU.
Artificial neural networks (ANNs) have revolutionized the field of science in the last few decades. Unlike classical machine learning (ML) algorithms, which require human effort to craft well-structured features, an A...
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Extensive scientific investigation is necessary because every government wants to construct smart cities. This is why examining how researchers approach this area of study is critical. This study investigates global r...
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This paper proposes a cyber security strategy for cyber-physical systems(CPS)based on Q-learning under unequal cost to obtain a more efficient and low-cost cyber security defense strategy with misclassification *** sy...
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This paper proposes a cyber security strategy for cyber-physical systems(CPS)based on Q-learning under unequal cost to obtain a more efficient and low-cost cyber security defense strategy with misclassification *** system loss caused by strategy selection errors in the cyber security of CPS is often considered ***,sometimes the cost associated with different errors in strategy selection may not always be the same due to the severity of the consequences of ***,unequal costs referring to the fact that different strategy selection errors may result in different levels of system losses can significantly affect the overall performance of the strategy selection *** introducing a weight parameter that adjusts the unequal cost associated with different types of misclassification errors,a modified Q-learning algorithm is proposed to develop a defense strategy that minimizes system loss in CPS with misclassification interference,and the objective of the algorithm is shifted towards minimizing the overall ***,simulations are conducted to compare the proposed approach with the standard Q-learning based cyber security strategy method,which assumes equal costs for all types of misclassification *** results demonstrate the effectiveness and feasibility of the proposed research.
We present the models implemented by the NICA group for the Quantum Computing (QuantumCLEF) Shared Task at CLEF 2024. Our participation focused on Task 1A: Feature Selection (Information Retrieval Task). We propose a ...
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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...
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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
Classification of malwares and viruses is a very important work in the cyber security field to protect the computers and systems from threats and attacks. In this paper, we proposed a novel approach for the classifica...
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The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally diffi...
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The coalescence and missed detection are two key challenges in Multi-Target Tracking(MTT).To balance the tracking accuracy and real-time performance,the existing Random Finite Set(RFS)based filters are generally difficult to handle the above problems simultaneously,such as the Track-Oriented marginal Multi-Bernoulli/Poisson(TOMB/P)and Measurement-Oriented marginal Multi-Bernoulli/Poisson(MOMB/P)*** on the Arithmetic Average(AA)fusion rule,this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli(PMB)filter,which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with *** order to fuse the different PMB distributions,the Bernoulli components in different Multi-Bernoulli(MB)distributions are associated with each other by Kullback-Leibler Divergence(KLD)***,an adaptive AA fusion rule is designed on the basis of the exponential fusion weights,which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in ***,by comparing with the TOMB/P and MOMB/P filters,the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.
Edge computing has emerged as a transformative approach for reducing latency and enhancing network performance by placing computing resources closer to data sources and end users via edge nodes. This approach addresse...
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