CASH: correlation-aware scheduling to mitigate soft error impact on heterogeneous multicores

Jiao, Jiajia; Wang, Libao; Li, Yanxiang; Han, Dong‐Guk; Yao, Min; Li, Kuan-Ching; Jiang, Hai

doi:10.1080/09540091.2020.1758924

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…As future work, we intend to design and assess the configuration parameter’s standard values of MOTP and shorten the length of the OTP while ensuring the same security level [ 27 ], as well as investigate techniques for resource protection and recovery [ 28 , 29 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree

Yin

Guo

et al. 2020

Sensors

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The Time-based One-Time Password (TOTP) algorithm is commonly used for two-factor authentication. In this algorithm, a shared secret is used to derive a One-Time Password (OTP). However, in TOTP, the client and the server need to agree on a shared secret (i.e., a key). As a consequence, an adversary can construct an OTP through the compromised key if the server is hacked. To solve this problem, Kogan et al. proposed T/Key, an OTP algorithm based on a hash chain. However, the efficiency of OTP generation and verification is low in T/Key. In this article, we propose a novel and efficient Merkle tree-based One-Time Password (MOTP) algorithm to overcome such limitations. Compared to T/Key, this proposal reduces the number of hash operations to generate and verify the OTP, at the cost of small server storage and tolerable client storage. Experimental analysis and security evaluation show that MOTP can resist leakage attacks against the server and bring a tiny delay to two-factor authentication and verification time.

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Section: Discussionmentioning

confidence: 99%

An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree

Yin

Guo

et al. 2020

Sensors

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“…Unscented transformation, however, can be implemented only when the covariance matrix ensures a positive definite matrix, limiting FO-UKF in real applications. The development of big data processing algorithms such as artificial intelligence (Liu et al, 2020;Kheyrinataj & Nazemi, 2020) has provided new directions for development in various fields; to improve the accuracy of SOC estimation, we can build a more accurate battery by combining existing SOC estimation methods with data representation, big data processing and artificial intelligence algorithms (Jiang et al, 2021;Jiao et al, 2021;Kashkooli et al, 2019). model (Jiang et al, 2021;Jiao et al, 2021;Kashkooli et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The development of big data processing algorithms such as artificial intelligence (Liu et al, 2020;Kheyrinataj & Nazemi, 2020) has provided new directions for development in various fields; to improve the accuracy of SOC estimation, we can build a more accurate battery by combining existing SOC estimation methods with data representation, big data processing and artificial intelligence algorithms (Jiang et al, 2021;Jiao et al, 2021;Kashkooli et al, 2019). model (Jiang et al, 2021;Jiao et al, 2021;Kashkooli et al, 2019). In recent years, supercapacitors are suitable for fast and frequent charging and discharging due to their high power density and long cycle life, and more and more scholars are combining them with Li-ion batteries to solve the shortcomings of Li-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

et al. 2021

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The accuracy of state of charge estimation results will directly affect the performance of battery management system. Due to such, we focus in this article on the SOC estimation of Lithium-Ion batteries based on a fractional second-order RC model with free noninteger differentiation orders. For such an estimation, three Kalman filters are employed: the adaptive extended Kalman filter (AEKF), extended Kalman filter (EKF), and Unscented Kalman Filter (UKF). The Fractional-Order Model (FOM) parameters and differentiation orders are identified by the Particle Swarm Optimization (PSO) algorithm, and a pulsed-discharge test is implemented to verify the accuracy of parameter identification. The output voltage error of the FOM model is much less than that of the Integer-Order Model (IOM). The FOM model has lower root-mean square error (RMSE), the mean absolute error (MAE), and the maximum absolute error (MAXAE) of SOC estimation than the IOM model during the SOC estimation regardless of AEKF, EKF or UKF. Experimental results show that the FOM can simulate the polarisation on effect and charge-discharge characteristics of the battery more realistically, demonstrating that the SOC estimation based on FOM is more accurate and promising than the one based on the IOM when using the same Kalman filters.

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“…Estimates of effort below real values often lead to project cancellations or failures due to spent budgets. Additionally, this is a problem for both software companies that have already invested resources in projects and for clients who will not get the desired products or have to invest more (Goyal & Parashar, 2018;Jiang et al, 2021;Jiao et al, 2021;Qiu et al, 2021;Subasri et al, 2020). On the other hand, the estimated effort is higher than realistic conduct to uncompetitive project design bids, so software teams often fail to get and start a project.…”

Section: Introductionmentioning

confidence: 99%

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

Ranković

Ivanović

et al. 2021

Connection Science

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This paper proposes a new, improved COmmon Software Measurement International Consortium function point (COSMIC FP) method that uses Artificial Neural Network (ANN) architectures based on Taguchi's Orthogonal Array to estimate software development effort. The minimum magnitude relative error (MRE) to evaluate these architectures considering the cost effect function, the type of data used in the training, testing, and validation of the proposed models, was used. Applying the fuzzification and clustering method to obtain seven different datasets, we would like to achieve excellent reliability and accuracy of the obtained results. Besides examining the influence of four input values, we aim to reduce the risks of potential errors, increase the coverage of a wide range of different projects and increase the efficiency and success of completing many various software projects. The main contributions of our work are as follows: the influence of four input values of the COSMIC FP method on the change of mean (MRE), development of two simple ANN architectures, the attainment of a small number of performed iterations in software effort estimation (less than 7), reduced software effort estimation time, the use of different values of the International Software Benchmarking Standards Group and other datasets used in the experiment.

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CASH: correlation-aware scheduling to mitigate soft error impact on heterogeneous multicores

Cited by 6 publications

References 45 publications

An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree

An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree

State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

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