Translation, Abstraction and Integration for Effective Smart System Design

Lora, Michele; Vinco, Sara; Fummi, Franco

doi:10.1109/tc.2019.2909209

Cited by 20 publications

(8 citation statements)

References 40 publications

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“…Lora et al proposed a middle encounter method to create a virtual platform of heterogeneous systems and automatically integrate the component model into a single homogeneous system level executable description. Through the analysis of typical design processes, the classification of the design domain/abstraction level is defined [ 23 ]. The purpose of Dhanjal and Singh research work is to develop an automatic system to translate speech into Indian sign language using avatar (SISLA).…”

Section: Related Workmentioning

confidence: 99%

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Sun

2022

Computational Intelligence and Neuroscience

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With the advent of the information age, people can establish good communication through Internet technology. Mechanical translation has become a key means to solve people’s communication problems. However, there are still obstacles to communication between different languages. In order to solve this problem, this paper uses existing neural network technology to the English-Chinese bidirectional machine translation model in the field of marine science and technology. Based on deep learning technology, we collect Chinese and English abstracts and partial full texts of Chinese and English papers with marine science and technology as the key words and build a professional corpus in English and Chinese about marine science and technology. In the Chinese-English bidirectional translation model, the local weight sharing is introduced into the Chinese encoder and the English encoder, and the output of the Chinese encoder sublayer and the English encoder sublayer is fused as the output of the respective encoders, and the performance of the translation model is evaluated using the BLEU parameters. Through the training of the translation model, compared with the transformer model, the BLEU value of the model with local weight sharing and encoder sublayer fusion output is improved by 1.6 and 3.8 in the Chinese-English and English-Chinese translation directions, respectively. The PPL values in the Chinese-English and English-Chinese translation directions decreased by 18.72% and 14.62%, respectively. We demonstrate the effectiveness of the language translation model. Experiments show that the research on machine language adaptive technology based on deep learning can more smoothly realize the two-way translation of literature in the field of marine science and technology. Compared with traditional mechanical translation, this paper proposes a translation model based on the deep neural algorithm, which improves the effect of model training by constructing a Chinese-English corpus with the theme of marine science and technology.

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Section: Related Workmentioning

confidence: 99%

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Sun

2022

Computational Intelligence and Neuroscience

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“…However, the existing frameworks either deal with the static or dynamic verification at a time. On the other hand, the complexity of modern embedded systems is significantly increased due to a higher degree of heterogeneity in terms of both software and hardware components [39]. As a result, the complex designs usually require the application of both (static as well as dynamic) types collectively in a unified design environment, to achieve the maximum verification coverage in an optimum time [7].…”

Section: A Limitations Of Existing Mbse Frameworkmentioning

confidence: 99%

A Unified Model-Based Framework for the Simplified Execution of Static and Dynamic Assertion-Based Verification

et al. 2020

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The improved productivity and reduced time-to-market are essential requirements for the development of modern embedded systems and, therefore, the comprehensive as well as timely design verification is critical. Assertion Based Verification (ABV) is a renowned paradigm to timely achieve an optimum test coverage, either through static or dynamic techniques. However, the major limitation with ABV is its inherited low-level implementation complexity. In order to simplify its execution, various Model Based System Engineering approaches provide a higher abstraction layer. Nevertheless, the complete verification requirements, targeting the static as well as dynamic ABV at the same time in a unified framework, are not being addressed. Furthermore, the dynamic verification support is provided through some traditional languages (like C, Verilog) where the advanced ABV features cannot be exploited. Consequently, this article introduces the MODEVES (MOdel-based DEsign Verification for Embedded Systems) framework to simultaneously support the static and dynamic ABV. Particularly, the UML (Unified Modeling Language) and SysML (Systems Modeling Language) diagrams are used to model the structural and behavioral requirements. Moreover, the NLCTL (Natural Language for Computation Tree Logic) is proposed to include the verification requirements for static ABV while the SVOCL (SystemVerilog in Object Constraint Language) is used to represent the dynamic verification constraints. An open source transformation engine is developed to automatically generate the SystemVerilog Register Transfer Level (RTL) code, Timed Automata model, SystemVerilog assertions and Computation Tree Logic (CTL) assertions with minimum transformation losses. The significance of the MODEVES framework is established through several case studies and the quantitative analysis shows an improvement of almost 100% in design productivity, as compared to the conventional low-level implementations.

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“…and simulations of complex systems. Smart systems are equivalent to IoT systems in these contexts as is presented recently in [16], [17].…”

Section: ) Research: System-level Descriptions Of Intelligent-smart mentioning

confidence: 99%

Classification Algorithms Framework (CAF) to Enable Intelligent Systems Using JetBrains MPS Domain-Specific Languages Environment

2020

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This paper describes the design and development of a Classification Algorithms Framework (CAF) using the JetBrains MPS domain-specific languages (DSLs) development environment. It is increasingly recognized that the systems of the future will contain some form of adaptivity therefore making them intelligent systems as opposed to the static systems of the past. These intelligent systems can be extremely complex and difficult to maintain. Descriptions at higher-level of abstraction (systemlevel) have long been identified by industry and academia to reduce complexity. This research presents a Framework of Classification Algorithms at system-level that enables quick experimentation with several different algorithms from Naive Bayes to Logistic Regression. It has been developed as a tool to address the requirements of British Telecom's (BT's) data-science team. The tool has been presented at BT and JetBrains MPS and feedback has been collected and evaluated. Beyond the reduction in complexity through the system-level description, the most prominent advantage of this research is its potential applicability to many application contexts. It has been designed to be applicable for intelligent applications in several domains from business analytics, eLearning to eHealth, etc. Its wide applicability will contribute to enabling the larger vision of Artificial Intelligence (AI) adoption in context.

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Translation, Abstraction and Integration for Effective Smart System Design

Cited by 20 publications

References 40 publications

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

A Unified Model-Based Framework for the Simplified Execution of Static and Dynamic Assertion-Based Verification

Classification Algorithms Framework (CAF) to Enable Intelligent Systems Using JetBrains MPS Domain-Specific Languages Environment

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