Minimum storage regenerating (MSR) codes are a class of maximum distance separable (MDS) array codes capable of repairing any single failed node by downloading the minimum amount of information from each of the helper...
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Machine Learning and Natural Language Processing are playing an increasingly vital role in many different areas, including cybersecurity in Information Technology and Operational Technology networking, with many assoc...
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Machine Learning and Natural Language Processing are playing an increasingly vital role in many different areas, including cybersecurity in Information Technology and Operational Technology networking, with many associated research challenges. In this paper, we introduce a new language model based on transformers with the addition of syntactical information into the embedding process. We show that our proposed Structurally Enriched Transformer (SET) language model outperforms baseline datasets on a number of downstream tasks from the GLUE benchmark. Our model improved CoLA classification by 11 points over the BERT-Base model. The performance of attention-based models has been demonstrated to be significantly better than that of traditional algorithms in several NLP tasks. Transformers are comprised of multi attention heads stacked on top of each others. A Transformer is capable of generating abstract representations of tokens input to an encoder based on their relationship to all tokens in a sequence. Despite the fact that such models can learn syntactic features based on examples alone, researchers have found that explicitly feeding this information to deep learning models can significantly boost their performance. A complex model like transformers may benefit from leveraging syntactic information such as part of speech (POS).
We carry out a numerical study on coherent control of multi-phase with SPGD and CMAES algorithms. We compare their performances and identify the distinct characteristic of each algorithm when used for phase control. &...
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Given a real dataset and a computation family, we wish to encode and store the dataset in a distributed system so that any computation from the family can be performed by accessing a small number of nodes. In this wor...
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Customers' load profiles are critical resources to support data analytics applications in modern power systems. However, there are usually insufficient historical load profiles for data analysis, due to the collec...
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Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning...
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ISBN:
(数字)9798350308365
ISBN:
(纸本)9798350308372
Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning approach combined with a multi-objective genetic algorithm (STMO-GA) for feature selection. Firstly, for the multi-objective component of our method, we employ a Non-dominated Sorting Genetic Algorithm (NSGA-II) to generate a Pareto front. Then, features are ranked based on their number of appearances in the same Pareto front. Next, during the sequential knowledge transfer process, the ranked features are iteratively selected until a predetermined
$n$
number of features remains. This feature subspace is further refined by a k-fold cross-validation operation, starting from the rank-one feature, to determine the cut-off of the
$n$
features that will remain. Comparative evaluations against both GA-based as well as traditional feature selection methods demonstrate that the proposed method achieves superior classification accuracy, while retaining the smallest number or a comparable number of features.
Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road safety globally. As intelligent transportation systems evolve, accurate and real-time identification of driver distract...
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The design space of current quantum computers is expansive with no obvious winning solution. This leaves practitioners with a clear question: "What is the optimal system configuration to run an algorithm?". ...
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A robust and scalable crowd management infrastructure is crucial in addressing operational challenges when deploying high-density sensors and actuators in a smart city. While crowdsourcing is widely used in crowd mana...
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A robust and scalable crowd management infrastructure is crucial in addressing operational challenges when deploying high-density sensors and actuators in a smart city. While crowdsourcing is widely used in crowd management, conventional solutions, such as Upwork and Amazon Mechanical Turk, generally depend on a trusted third-party platform. There exist several potential security concerns(e.g., sensitive leakage, single point of failure and unfair judgment) in such a centralized paradigm. Hence, a recent trend in crowdsourcing is to leverage blockchain(a decentralized ledger technology) to address some of the existing limitations. A small number of blockchain-based crowdsourcing systems(BCSs) with incentive mechanisms have been proposed in the literature, but they are generally not designed with security in mind. Thus, we study the security and privacy requirements of a secure BCS and propose a concrete solution(i.e., SecBCS)with a prototype implementation based on JUICE.
Active microwave thermography (AMT) is an integrated nondestructive testing and evaluation (NDT&E) technique that features a microwave-based excitation and subsequent thermographic inspection via an infrared camer...
Active microwave thermography (AMT) is an integrated nondestructive testing and evaluation (NDT&E) technique that features a microwave-based excitation and subsequent thermographic inspection via an infrared camera. AMT has been successfully employed in several industries including aerospace and civil for NDT&E inspections. Since the excitation is microwave-based, an antenna is used to irradiate the sample under test and hence the heating pattern will vary spatially (following the antenna pattern). This nonuniform thermal excitation may limit the ability of AMT to quantify defect cross-sections. Therefore, this work seeks to expand the capabilities of AMT by incorporating a post-processing technique to improve defect cross-section quantification. Specifically, an approach based on the temperature gradient is considered, with results compared to other well-established approaches. The effect of noise is also considered. The results, from both simulation and measurement, indicate that the temperature gradient approach provides the least amount of error in defect cross-section quantification.
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