scholarly journals Performance Models for Heterogeneous Iterative Programs

2022 ◽  
Vol 12 (1) ◽  
pp. 131-163
Author(s):  
Aparna Sasidharan
Keyword(s):  
2000 ◽  
Vol 147 (3) ◽  
pp. 61 ◽  
Author(s):  
V. Cortellessa ◽  
G. Iazeolla ◽  
R. Mirandola

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 28
Author(s):  
Anna V. Kalyuzhnaya ◽  
Nikolay O. Nikitin ◽  
Alexander Hvatov ◽  
Mikhail Maslyaev ◽  
Mikhail Yachmenkov ◽  
...  

In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models’ design and co-design, the generalized formulation of the modeling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models. Moreover, the involvement of the performance models in the design process is analyzed. A set of experiments with various models and computational resources is conducted to verify different aspects of the proposed approach.


2021 ◽  
pp. 1-12
Author(s):  
Yingwen Fu ◽  
Nankai Lin ◽  
Xiaotian Lin ◽  
Shengyi Jiang

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.


Author(s):  
Richard Steinberg ◽  
Raytheon Company ◽  
Alice Diggs ◽  
Raytheon Company ◽  
Jade Driggs

Verification and validation (V&V) for human performance models (HPMs) can be likened to building a house with no bricks, since it is difficult to obtain metrics to validate a model when the system is still in development. HPMs are effective for performing trade-offs between the human system designs factors including number of operators needed, the role of automated tasks versus operator tasks, and member task responsibilities required to operate a system. On a recent government contract, our team used a human performance model to provide additional analysis beyond traditional trade studies. Our team verified the contractually mandated staff size for using the system. This task demanded that the model have sufficient fidelity to provide information for high confidence staffing decisions. It required a method for verifying and validating the model and its results to ensure that it accurately reflected the real world. The situation caused a dilemma because there was no actual system to gather real data to use to validate the model. It is a challenge to validate human performance models, since they support design decisions prior to system. For example, crew models are typically inform the design, staffing needs, and the requirements for each operator’s user interface prior to development. This paper discusses a successful case study for how our team met the V&V challenges with the US Air Force model accreditation authority and successfully accredited our human performance model with enough fidelity for requirements testing on an Air Force Command and Control program.


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