Computational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typi...
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Computational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. It is nowadays considered a fundamental skill for students and citizens, that has the potential to affect future generations. At the roots of computational-thinking abilities stands the knowledge of computer programming, i.e. coding. With the goal of fostering computational thinking in young students, we address the challenging and open problem of using methods, tools and techniques to support teaching and learning of computer-programming skills in school curricula of the secondary grade and university courses. This problem is made complex by several factors. In fact, coding requires abstraction capabilities and complex cognitive skills such as procedural and conditional reasoning, planning, and analogical reasoning. In this paper, we introduce a new paradigm called ACME ("Code Animation by Evolved Metaphors") that stands at the foundation of the Diogene-CT code visualization environment and methodology. We develop consistent visual metaphors for both procedural and object-oriented programming. Based on the metaphors, we introduce a playground architecture to support teaching and learning of the principles of coding. To the best of our knowledge, this is the first scalable code visualization tool using consistent metaphors in the field of the Computing Education Research (CER). It might be considered as a new kind of tools named as code visualization environments.
In software development, the quality of identifier names is important because it greatly affects program comprehension for developers. However, naming identifiers that appropriately represent the nature or behavior of...
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ISBN:
(纸本)9781728146485
In software development, the quality of identifier names is important because it greatly affects program comprehension for developers. However, naming identifiers that appropriately represent the nature or behavior of program elements such as classes and methods is a difficult task requiring rich development experience and software domain knowledge. Although several studies proposed techniques for recommending identifier names, there are few studies targeting class names and they have limited availability. This paper proposes a novel class name recommendation approach widely available in software development. The key idea is to represent quantitatively the nature or behavior of classes by leveraging embedding technology for heterogeneous graphs. This makes it possible to recommend class names even where a previous approach cannot work. Experimental results suggest that the proposed approach can produce more accurate class name recommendation regardless of whether classes are used. In addition, a further experiment reveals a situation where the proposed approach is particularly effective.
Video data transmission without buffering is ubiquitous for all contemporary appliances. The increased consumption of video content has created the demand for more efficient video compression algorithms for high-resol...
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Video data transmission without buffering is ubiquitous for all contemporary appliances. The increased consumption of video content has created the demand for more efficient video compression algorithms for high-resolution videos. In this study, a video compression technique is presented that uses a Lagrangian encoder (LE) with H.265 protocol. This architecture provides compression with less bandwidth compared to contemporary encoders, and it is compatible with 5 G network transmission speed and latency. Adding LE in the H.265 protocol architecture reduced the buffering delay and increased the efficiency. Along with these parameters, this study focused on the quality of video streaming, which was measured by metrics such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), and video multi-method assessment fusion (VMAF). A comparison of the values of compression ratio and latency with and without H.264 architecture showed improved performance in the architecture. Thus, deep learning-based optimised data transmission can improve accuracy and reduce computational complexity.
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