Open Educational Resources (OERs) are often scattered among various sources and may follow different metadata schemata. In addition, they may not include exhaustive annotations;even worse, their subject characterizati...
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Simulation models are frequently used to model, simulate and test complex systems (e.g., Cyber-Physical Systems (CPSs)). To allow full test automation, test cases and test oracles are required. Safety standards (e.g.,...
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ISBN:
(数字)9781728110752
ISBN:
(纸本)9781728110769
Simulation models are frequently used to model, simulate and test complex systems (e.g., Cyber-Physical Systems (CPSs)). To allow full test automation, test cases and test oracles are required. Safety standards (e.g., the ISO 26262) highly recommend that the test cases of systems like CPSs are associated to requirements. As a result, typically, test cases that need to cover specific requirements are manually generated in the context of simulation models. This is, of course, a time-consuming and non-systematic process. However, the current practice lacks tools that generate test cases by considering functional requirements for simulation-based testing. In this short paper we propose a Domain-Specific Language (DSL) for specifying requirements for simulation-based testing in an easy manner. These files are later parsed by an automatic test generation algorithm, which generates test cases that follow the ASAM-XiL standard. The tool was integrated with two professional tools: (1) SYNECT from dSPACE and (2) xMOD from FEV. An initial validation was also performed with an industrial simulation model from YASA motors.
In this work, we present a unified model that can handle both Keyword Spotting and Word Recognition with the same network architecture. The proposed network is comprised of a non-recurrent CTC branch and a Seq2Seq bra...
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Regression problems have been widely studied in machine learning literature resulting in a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the pro...
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In this work, we perform a wide variety of experiments with different Deep Learning architectures in small data conditions. We show that model complexity is a critical factor when only a few samples per class are avai...
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Artificial intelligence (AI) is having a profound impact on human life, with both benefits and drawbacks in the societal, environmental, and technological realms. However, the ethical implications of AI are often not ...
Artificial intelligence (AI) is having a profound impact on human life, with both benefits and drawbacks in the societal, environmental, and technological realms. However, the ethical implications of AI are often not addressed in technology education, leaving future professionals with a lack of awareness in this area. This is concerning, as AI has the potential to greatly information delivery and affect human thinking, interaction, decision-making, and communication. To address these issues, there is a need for a framework to guide and help future AI developers make ethically responsible decisions. In this paper we propose a framework to foster ethical awareness and promote respect for human dignity and well-being, while also preventing harm. It is designed to be incorporated into technology education, ensuring that future professionals are equipped to navigate the ethical implications of AI. By prioritizing ethical reasoning in technology education, we can build a better and more responsible AI industry, ensuring that AI can provide benefits for society and does not cause harm. Additionally, a tech industry that values ethics and social responsibility will be better equipped to build technology that serves the public interest, rather than solely maximizing profits. Teaching ethical reasoning in technology education is a crucial step in preparing future professionals to make informed and ethical decisions in the development and use of AI systems. It will lead to a better and more responsible AI industry that benefits all of society.
In this paper, we propose a framework that uses latent information from Twitter images by employing the Google Cloud Vision API platform aiming at enriching social analytics with semantics and textual information. Our...
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ISBN:
(数字)9781728159195
ISBN:
(纸本)9781728159201
In this paper, we propose a framework that uses latent information from Twitter images by employing the Google Cloud Vision API platform aiming at enriching social analytics with semantics and textual information. Our study reveals that user-generated content, linked data as well as hidden concepts and textual information from social images can be highly considered for enriching social analytics. Finally, we publish our annotated dataset for further use and evaluation from our research community.
Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data. We prop...
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In complex software, such as various intelligent monitoring and control systems, the requirements for its reliability and safety increase. However, traditional reliability models cannot longer describe its reliability...
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In complex software, such as various intelligent monitoring and control systems, the requirements for its reliability and safety increase. However, traditional reliability models cannot longer describe its reliability behavior with sufficient accuracy. This paper presents an approach to software reliability assessment using high-order Markov chains on an example of CubeSat nanosatellites flight software. The method uses the developed algorithm of high-order Markov process representation through an equivalent first-order process. The simulation results show that high-order Markov chains can increase the accuracy of the failure rate assessment up to 50%. Hence, the usage of the high-order reliability models is obvious for accurate and reliable evaluation of software reliability and safety.
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